Staff Software Engineer, Data Enrichment Platform at Wayve in London, United Kingdom

Job description

• Application
• The role
• As a Staff Software Engineer for the Data Enrichment Platform, you will own the technical vision for the systems that enable Wayve’s teams to deploy, run, evaluate and continually improve machine-learning models. You will shape a platform that processes petabytes of driving data, operates across large GPU fleets and supports dozens of model engineers. Working across model engineering, infrastructure, compute and data teams, you will create dependable, self-service capabilities that improve model quality while reducing cost and operational effort.

Responsibilities

• Define the technical vision and architecture for Wayve’s end-to-end Data Enrichment Platform.
• Lead the development of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities and dataset catalogues.
• Build scalable and reliable systems for large-scale inference, automated evaluation, model monitoring, active learning and retraining.
• Partner with platform teams to address data locality, scheduling, capacity, backpressure, failure recovery, observability and cost across petabyte-scale datasets and large GPU fleets.
• Make effective build, buy, reuse, integrate, consolidate and replace decisions as Wayve’s requirements and the technology landscape evolve.
• Establish clear technical boundaries and working relationships across AI Platform, infrastructure, compute, storage, data and model-engineering teams.
• Remain hands-on while leading cross-team delivery, mentoring engineers and driving the adoption of reusable, self-service platform capabilities.
• Define measurable service levels and success metrics covering platform reliability, throughput, cost, automation and adoption.

Requirements

• In order to set you up for success as a Staff Software Engineer for the Data Enrichment Platform at Wayve, we’re looking for the following skills and experience.
• Experience owning the long-term technical direction and measurable outcomes of a complex, multi-system platform or capability.
• Strong production software-engineering experience in Python, including building maintainable, tested and observable backend services, APIs and data-processing systems.
• Deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads.
• A track record of productising internal platforms for broad adoption, with a focus on reliability, usability and self-service.
• Excellent architecture and technology judgement, combined with the ability to remain hands-on and lead delivery across organisational boundaries.
• Strong communication and influencing skills, with experience mentoring senior engineers and aligning multiple technical teams.
• Experience with workflow-orchestration technologies such as Flyte, Airflow, Dagster or Argo.
• Experience building large-scale batch-inference or ML-platform systems, including model deployment and model registries.
• Knowledge of Spark, Databricks, Ray or comparable distributed-compute technologies.
• Experience with annotation or dataset-catalogue platforms, including metadata, provenance, versioning, lineage and quality control.
• Experience with Kubernetes, GPU infrastructure and cost-aware cloud architecture.
• Experience implementing continuous evaluation, model-quality monitoring, active-learning or automated retraining workflows.
• Experience designing or operating production platforms for robotics or computer-vision workloads, with an understanding of how data, annotation, inference, evaluation and monitoring fit together. Relevant workloads could include object detection, depth estimation, segmentation, tracking, sensor data or 3D models; computer-vision research expertise is not required.

Skills

Required

• Python
• Backend services
• APIs
• Data-processing systems
• Distributed systems
• Architecture
• Mentoring
• Communication
• Influencing

Preferred

• Flyte
• Airflow
• Dagster
• Argo
• Spark
• Databricks
• Ray
• Kubernetes